Optical systems for biological and biochemical reactions have been used to monitor, measure, and/or analyze such reactions in real time. Such systems are commonly used in sequencing, genotyping, polymerase chain reaction (PCR), and other biochemical reactions to monitor the progress and provide quantitative data. For example, an optical excitation beam may be used in real-time PCR (qPCR) reactions to illuminate hybridization probes or molecular beacons to provide fluorescent signals indicative of the amount of a target gene or other nucleotide sequence. Increasing demands to provide greater numbers of reactions per test or experiment have resulted in instruments that are able to conduct ever higher numbers of reactions simultaneously.
The increase in the number sample sites in a test or experiment has led to microtiter plates and other sample formats that provide ever smaller sample volumes. In addition, techniques such as digital PCR (dPCR) have increased the demand for smaller sample volumes that contain either zero or some positive number (e.g., 1, 2, 3, etc) of target nucleotide sequence in all test samples.
Furthermore, generally, there is an increasing need to automate systems to increase efficiency. For example, advances in automated biological sample processing instruments allow for quicker, more efficient, and high throughput analysis of samples. These types of systems may assay a greater number of samples than previous systems. As such, samples undergoing various assays are labeled or marked with identifiers.
Previously, an operator of the system or instrument may have had to manually track and validate samples by reading the identifiers on sample containers, racks, or assay chips. This type of manual tracking and validation can be labor-intensive and include a high probability of operator error such as sample mistracking, or improper testing. Furthermore, the greater number of samples desired to be assayed would be more time intensive and cumbersome.
Other more automated systems may scan for identifiers to track and validate samples before testing. However, these systems often need additional components. Furthermore, the identifiers may be misread or unreadable by the systems.
As such, with higher throughput systems performing detection and analysis on a large number of samples of small volumes” as the cumulative effect of system background noise becomes increasingly important as the volumes get smaller, it becomes increasingly important to remove background noise due to undesired emissions or physical artifacts in the system to be able to perform an accurate analysis.
Previously, images have been processed by first imaging a background substrate to generate data that can then be used to subtract from the image data generated by imaging a substrates including the region-of-interests.